Arc Start Adjustment Device, Welding System and Arc Start Adjustment Method

ABSTRACT

An arc start adjustment device adjusting an arc start procedure in a welding process comprises an obtainment unit that obtains welding data indicating a welding state during or after a welding process, and a procedure adjustment unit that adjusts the arc start procedure such that the cycle time of the welding process is shortened based on welding data obtained by the obtainment unit.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Nonprovisional application claims priority under 35 U.S.C. § 119(a)on Patent Application No. 2017-228249 filed in Japan on Nov. 28, 2017,the entire contents of which are hereby incorporated by reference.

FIELD

The present invention relates to an arc start adjustment device thatadjusts an arc start procedure in a welding process, a welding systemand an arc start adjustment method.

BACKGROUND

A consumable electrode type gas shield arc welding method in known as akind of welding methods. The gas shield arc welding method is a methodin which an electric arc is caused between a welding wire fed to aregion to be welded of a base metal and the base metal, and the basemetal is welded by the heat of the electric arc. The method isparticularly for performing welding with shield gas injected around theregion to be welded in order to protect the base metal heated to hightemperature from oxidation.

In the case of performing arc welding by the use of a welding robot, anarc is caused by an arc start procedure in which a welding torch ismoved to a welding start position, then a wire starts to be slowed down,and welding current is supplied at the timing when the welding wire isin contact with a base metal. Here, since the welding robot starts tomove the welding torch after an arc forms, the robot is uselesslystopped for a period from when the welding torch reaches the weldingstart position to when the arc forms, which is a cause of decrease inproductivity.

As a technique to solve such a problem, Japanese Patent ApplicationLaid-Open No. H10 (1998)-244483 discloses a technique in which arc startprocessing has been started before the welding torch reaches the weldingstart position, so that productivity is improved. More specifically,slowing down the wire feeding in advance, setting a wire slow down speedto high, or the like enable shortening of the time required for the arcstart processing.

It is noted that Japanese Patent Application Laid-Open No. 2017-30014discloses a technique in which welding conditions are automatically setby machine learning using image data obtained by imaging a weldingregion, outer appearance data of a weld bead obtained by processing thisimage data, data on an amount of generated spatter, and the like.

Furthermore, Japanese Patent Application Laid-Open No. 2017-39160discloses a technique in which the quality determination of a weldingresult is performed based on welding monitor data such as weldingcurrent, welding voltage, wire feeding speed, etc. that are measuredduring the welding process.

SUMMARY

However, in the case where the arc start processing is executed inadvance, the welding quality may be deteriorated due to various factors.In order to ensure the welding quality, the operator is required torepeatedly perform arc start processing manually and perform setting toconditions that appear to be optimum from a number of test results.

An object of the present disclosure is to provide an arc startadjustment device that is capable of automatically shortening the cycletime of a welding process, especially the time from when the weldingtorch reaches the welding start position to when the arc forms.

An arc start adjustment device according to the present disclosure beingan arc start adjustment device adjusting an arc start procedure in awelding process comprises: an obtainment unit that obtains welding dataindicative of a welding state during or after a welding process, and aprocedure adjustment unit that adjusts the arc start procedure such thata cycle time of the welding process is shortened based on the weldingdata obtained by the obtainment unit.

The arc start adjustment method according to the present disclosure isan arc start adjustment method of adjusting an arc start procedure in awelding process, and comprises: obtaining welding data indicative of awelding state during or after the welding process; and adjusting the arcstart procedure such that a cycle time of the welding process isshortened based on obtained welding data.

According to the present disclosure, it is possible to automaticallyshorten the cycle time of a welding process, especially the timerequired from when the welding torch reaches the welding start positionto when the arc forms.

The above and further objects and features will more fully be apparentfrom the following detailed description with accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view depicting an arc welding system according toEmbodiment 1.

FIG. 2 is a block diagram depicting an arc start adjustment deviceaccording to Embodiment 1.

FIG. 3 is a conceptual view depicting an arc start procedure.

FIG. 4 is a functional block diagram depicting the arc start adjustmentdevice according to Embodiment 1.

FIG. 5 is a flowchart depicting an arc start adjustment method accordingto Embodiment 1.

FIG. 6 is a functional block diagram depicting an arc start adjustmentdevice according to Embodiment 2.

FIG. 7 is a conceptual view depicting a network structure of a procedureadjustment unit.

FIG. 8 is a functional block diagram depicting an arc start adjustmentdevice according to Embodiment 3.

FIG. 9 is a functional block diagram depicting an arc start adjustmentdevice according to Embodiment 4.

DETAILED DESCRIPTION

Embodiments of the present disclosure are concisely listed. It is notedthat at least parts of the embodiments described below may arbitrarilybe combined.

An arc start adjustment device according to the present disclosure beingan arc start adjustment device adjusting an arc start procedure in awelding process comprises: an obtainment unit that obtains welding dataindicative of a welding state during or after a welding process, and aprocedure adjustment unit that adjusts the arc start procedure such thata cycle time of the welding process is shortened based on the weldingdata obtained by the obtainment unit.

According to the present aspect, the obtainment unit obtains weldingdata, and the procedure adjustment unit adjusts an arc start procedurebased on the obtained welding data. The welding data is informationindicative of a welding state and includes information as to whether ornot the cycle time can be shortened, whether or not the cycle timeshould be extended, etc. The procedure adjustment unit can adjust thearc start procedure by using the welding data such that the cycle timeof the welding process is shortened without worsening the welding resultand can shorten the cycle time of the arc start procedure.

The arc start adjustment device according to the present disclosurefurther comprises a quality determination unit that determines whether awelding result is positive or negative based on the welding dataobtained by the obtainment unit, and the procedure adjustment unitdecides a change in the arc start procedure such that the cycle time isshortened if the quality determination unit determines that the weldingresult is positive, and the cycle time is extended if the qualitydetermination unit determines that the welding result is negative.

According to the present aspect, if the welding result is positive, thearc start adjustment device shortens the cycle time of the weldingprocess because there is a probability that the cycle time of thewelding process has room for being shortened. If the welding result isnegative, the arc start adjustment device extends the cycle time of thewelding process. By such adjustment processing, the cycle time of thewelding process can be shortened while the welding result is worsen aslittle as possible.

In the arc start adjustment device according to the present disclosure,the procedure adjustment unit finalizes the adjustment by the arc startprocedure before the cycle time is shortened if the welding resultchanges from a positive state to a negative state as a result ofshortening the cycle time of the welding process, and stores a finalizedarc start procedure in a storage device.

According to the present aspect, the cycle time of the welding processcan be minimized, and the storage device stores the arc start procedurewith the minimum cycle time. The minimum arc start procedure is notnecessarily an arc start procedure with the minimum cycle time that islogically obtained. The minimum arc start procedure means the arc startprocedure before the cycle time being shortened when the welding resultchanges from a positive state to a negative state as a result ofshortening the cycle time of the welding process.

Henceforth, the cycle time can immediately be shortened by using the arcstart procedure stored in the storage device.

In the arc start adjustment device according to the present disclosure,the quality determination unit includes a quality determination neuralnetwork that trains a neural network to output data indicating thequality of a welding result concerning a welding process when thewelding data is obtained in a case where the welding data is input.

According to the present aspect, the quality determination neuralnetwork which is, for example, a learned deep neural network and canappropriately determine the quality of the welding result. The kind ofthe neural network is not limited to a specific one but may be selectedfrom a convolutional neural network (CNN), a recurrent neural network(RNN), a long short-term memory (LSTM) or the like depending on thefeatures of the welding data.

In the arc start adjustment device according to the present disclosure,the procedure adjustment unit includes a procedure adjustment neuralnetwork that trains a neural network to output data indicating a changein the arc start procedure that is capable of shortening the cycle timeof the welding process in a case where the welding data is input.

According to the present aspect, the procedure adjustment neural networkis, for example, a learned deep neural network and can appropriatelyadjust the arc start procedure. The kind of the neural network is notlimited to a specific one but may be selected from CNN, RNN, LSTM or thelike depending on the features of the welding data.

In the arc start adjustment device according to the present disclosure,the procedure adjustment neural network outputs data indicative of achange amount of the arc start procedure.

According to the present aspect, the procedure adjustment neural networkcan output not data as to whether or not the cycle time of the weldingprocess is to be shortened but data as to the change amount of theadjustable arc start procedure. For example, the procedure adjustmentneural network can output a large change amount if the welding result isextremely stable and output a small change amount if the welding resultis positive but not stable. Accordingly, the cycle time of the weldingprocess can be shortened more promptly.

The arc start adjustment device according to the present disclosurefurther comprises: a quality determination unit that determines whethera welding result is positive or negative based on the welding dataobtained by the obtainment unit; and a learning processing unit thattrains the procedure adjustment neural network based on a resultdetermined by the quality determination unit obtained after the arcstart procedure is adjusted.

According to the present aspect, the procedure adjustment neural networkis trained by using the data indicative of the welding result when thearc start procedure is adjusted. Accordingly, the cycle time of thewelding process can be shortened more effectively in order that thewelding result is not worsen.

In the arc start adjustment device according to the present disclosure,the learning processing unit trains the procedure adjustment neuralnetwork such that the cycle time is shortened if the qualitydetermination unit determines that the determination result is positivewhile the cycle time is extended if the quality determination unitdetermines that the determination result is negative.

According to the present aspect, the procedure adjustment neural networkcan be trained in the direction in which the cycle time of the weldingprocess is shortened. By the training, the cycle time of the weldingprocess can be minimized.

In the arc start adjustment device according to the present disclosure,the learning processing unit trains the procedure adjustment neuralnetwork such that the cycle time is maintained if the welding result isin a moderate state between positive and negative states.

According to the present aspect, the procedure adjustment neural networkcan be trained such that the cycle time of the welding process can bemaintained if the welding result is in a moderate state between positiveand negative states. The moderate state is a state where the weldingresult is relatively good but is likely to be worsen if the cycle timeis shortened any further. By the training, the cycle time of the weldingprocess can be minimized, and the welding result can be stabilized at apositive state.

In the arc start adjustment device according to the present disclosure,the quality determination unit includes a quality determination neuralnetwork that trains a neural network to output data indicating thequality of a welding result concerning a welding process when thewelding data is obtained in a case where the welding data is input.

According to the present aspect, the quality determination neuralnetwork is, for example, a learned deep neural network, and canappropriately determine the quality of the welding result. By using thequality determination result from the quality determination neuralnetwork, the procedure adjustment neural network can be trained moreeffectively.

In the arc start adjustment device according to the present disclosure,the procedure adjustment neural network includes a network structuresubstantially the same as all or a part of the quality determinationneural network.

According to the present aspect, the procedure adjustment neural networkincludes a neuron structure substantially the same as all or a part ofthe quality determination neural network. For example, a part of theprocedure adjustment neural network has intermediate layers andweighting factors the same or substantially the same as whole or a partof the quality determination neural network. The quality determinationof the welding result and the adjustment of the arc start procedure havepartially common features, so that the quality determination neuralnetwork can be employed as the procedure adjustment neural network. Thatis, the initial values of the weighting factors of the procedureadjustment neural network can be set to more appropriate values.Accordingly, even if training data for training the arc start procedureis not enough, if training data of the welding data and data indicativeof the quality of the welding result, i.e., whether the welding resultis positive or negative may be sufficiently prepared, the initial valuesof the weighting factor of the procedure adjustment neural network canappropriately be set, so that the procedure adjustment neural networkcan efficiently be trained. Needless to say, the procedure adjustmentneural network and the quality determination neural network may have thesame network structure.

The arc start adjustment device according to the present disclosurecomprises a quality determination unit that determines the quality ofthe welding result based on welding data obtained by the obtainmentunit, and a state data obtainment unit that obtains state data includingimage data obtained by imaging a welding torch, a welding wire and abase metal at multiple time points in arc start processing. Theprocedure adjustment unit comprises: an evaluation unit that calculates,based on state data obtained by the state data obtainment unit andaction data indicative of an action concerning the arc start procedure,an evaluation value for the action in a state indicated by the statedata; an action selection unit that selects an action for which anevaluation value calculated by the evaluation unit is maximum; a rewardcalculation unit that calculates a reward for the arc start procedurebased on a result determined by the quality determination unit obtainedafter the arc start procedure is adjusted and a time from when thewelding torch reaches a welding region to when an arc forms; and areinforcement learning unit that trains the evaluation unit based on thestate data obtained by the state data obtainment unit, the action dataindicative of the action concerning the arc start procedure and thereward calculated by the reward calculation unit.

According to the present aspect, reinforcement learning of the arc startprocedure with the shortened cycle time of the welding process is madepossible.

In the arc start adjustment device according to the present disclosure,the evaluation unit comprises an evaluation neural network to output anevaluation value for the action in the state indicated by the state datain the case where the state data obtained by the state data obtainmentunit and the action data indicative of the action concerning the arcstart procedure are input.

According to the present aspect, deep reinforcement learning of the arcstart procedure with the shortened cycle time of the welding process ismade possible.

In the arc start adjustment device according to the present disclosure,the welding data includes data indicative of at least one of weldingcurrent and welding voltage detected during the welding process, afeeding speed of a welding wire, a short-circuit situation, a weldingsound collected during the welding process and an image of a weldingregion imaged after the welding process.

According to the present aspect, the arc start procedure can be adjustedby using the data indicative of at least one of welding current andwelding voltage detected during the welding process, a feeding speed ofthe welding wire, a short-circuit situation, a welding sound collectedduring the welding process and an image of a welding region imaged afterthe welding process.

In the arc start adjustment device according to the present disclosure,the arc start procedure includes at least one of a start timing ofslowing down the wire feeding, a wire slow down speed, a start timing ofsupplying welding current and a welding current value at the start offorming an arc.

According to the present aspect, the cycle time of the welding processcan be shortened by adjusting a start timing of slowing down the wirefeeding, a wire slow down speed, a start timing when welding current issupplied or a welding current value when an arc starts.

A welding system according to the present disclosure comprises the arcstart adjustment device according to any one of the above mentioneddevices; a welding robot with a welding torch; and a welding powersource that supplies welding current to the welding torch.

According to the present aspect, the welding system including thewelding robot and the welding power source can shorten the cycle time ofthe welding process. Note that the arc start adjustment device may beprovided inside the welding robot and the welding power source, may beprovided inside a control device to control the operations of thewelding robot and welding power source, or may be provided as a separatedevice external to the welding robot, the welding power source and thecontrol device. Alternatively, the arc start adjustment device may be aserver while the control device or the welding power source maycommunicate with the server to shorten the cycle time of the weldingprocess.

The arc start adjustment method according to the present disclosure isan arc start adjustment method of adjusting an arc start procedure in awelding process, and comprises: obtaining welding data indicative of awelding state during or after the welding process; and adjusting the arcstart procedure such that a cycle time of the welding process isshortened based on obtained welding data.

According to the present aspect, the cycle time of the welding processcan be shortened. The arc start adjustment method may automatically beimplemented by the welding power source, the control device, or the likeconstituting the welding system, or the arc start adjustment method maybe implemented by the operator connecting the arc start adjustmentdevice to the welding system.

The present disclosure will be described in detail below with referenceto the drawings depicting embodiments. Moreover, at least parts ofembodiments that will be described below may arbitrarily be combined.

Embodiment 1

FIG. 1 is a schematic view depicting an arc welding system according toEmbodiment 1. The arc welding system according to the present embodimentis a consumable electrode type gas shield arc welding machine andincludes a welding robot 1, a welding power source 2, a control device3, an imaging device 4 and an arc start adjustment device 5. The arcstart adjustment device 5 is provided in the control device 3.

The welding robot 1 is for automatically performing arc welding of abase metal A. The welding robot 1 is provided with a base portion fixedat an appropriate position on a floor surface. The base portion isrotatably coupled with multiple arms via shaft portions, and at the tipend portion of the arms, a welding torch 11 is held. Furthermore, at anappropriate position of the arms, a wire feed device 12 is provided. Thecoupled portion between the arms is provided with a motor, and each armis rotated about the shaft portion by a rotational driving force of themotor. The rotation of the motor is controlled by the control device 3.The control device 3 can move the welding torch 11 in the upper, lower,back, forth, right and left directions relative to the base metal A byrotating each of the arms. Moreover, the coupled portion between thearms is provided with an encoder to output a signal indicative of arotating position of the arm to the control device 3, and the controldevice 3 recognizes the position of the welding torch 11 based on thesignal output from the encoder.

The welding torch 11 is made of an electrically conducting material suchas a copper alloy or the like, and guides a welding wire W to the basemetal A which is to be welded and has a cylindrical contact tip tosupply welding current required for forming arc. The welding current issupplied from the welding power source 2. The welding wire W is fed tothe welding torch 11 from a wire feed source (not illustrated) by a wirefeed device 12. The welding wire W is, for example, a solid wire andfunctions as a consumable electrode.

The contact tip is in contact with the welding wire W insertedtherethrough and supplies welding current to the welding wire W.Furthermore, the welding torch 11 has a nozzle, which has a hollowcylindrical shape so as to enclose the contact tip, and injects shieldgas to the base metal A through the opening at the tip end portionthereof. The shield gas is to protect the base metal A and welding wireW that are molten by an arc from oxidization. The shield gas is inertgas such as carbon dioxide, a mixture of carbon dioxide and argon gas,argon or the like. The shield gas is supplied from the welding powersource 2.

The welding power source 2 is provided with a power source 21, a wirefeed controller 22, a shield gas supply device 23 and a detector 24. Thepower source 21 is connected to the contact tip of the welding torch 11and the base metal A via a power supply cable and supplies weldingcurrent. The wire feed controller 22 controls the feeding speed of thewelding wire W by the wire feed device 12. The shield gas supply device23 supplies shield gas to the welding torch 11. The detector 24 includesa current detector to detect welding current flowing through an arcduring the welding process and a voltage detector to detect voltageapplied to the welding torch 11 and the base metal A. The power source21 includes a power source circuit to output direct current that issubjected to PWM control based on the welding current and weldingvoltage detected by the detector 24, and also includes a signalprocessing circuit and so on. Moreover, the welding power source 2outputs to the control device 3 welding monitor data indicative of awelding state during the welding process. The welding monitor data is,for example, welding current data or welding voltage data respectivelyindicating the welding current and the welding voltage detected duringthe welding process. In addition, as welding monitor data, feeding speeddata indicative of the feeding speed of the welding wire W,short-circuit situation data indicative of the short-circuit situation,welding sound data collected by a microphone (not illustrated) may beoutput to the control device 3.

The imaging device 4 images a welding region of the base metal A after awelding process and outputs image data obtained by imaging to thecontrol device 3.

The control device 3 controls the operation of the welding robot 1 aswell as controls the operation of the welding power source 2 byoutputting to the welding power source 2 welding conditions such aswelding current, welding voltage, the feeding speed of the welding wireW, an amount of supplied shield gas, and so on. The control device 3stores various welding conditions concerning the material of the basemetal A, the kind of a groove and so on. Moreover, the control device 3outputs an arc start procedure to thereby execute arc start processing.The above-described welding conditions stored by the control device 3are not necessarily optimum ones, and the arc start procedure isadjusted by the arc start adjustment device 5 such that the cycle timeof the welding process is minimized to the extent that the weldingresult is not worsen.

FIG. 2 is a block diagram depicting the arc start adjustment device 5according to Embodiment 1. The arc start adjustment device 5 is providedwith a controller 50 to control the operation of each of the componentsin the arc start adjustment device 5. The controller 50 is connected toan input unit 50 a, an output unit 50 b and a storage device 50 c. Theinput unit 50 a and the output unit 50 b are an input circuit and anoutput circuit respectively.

The storage device 50 c is a nonvolatile memory such as an electricallyerasable programmable ROM (EEPROM), a flash memory or the like. Thestorage device 50 c stores a computer program 50 d includinginstructions to minimize the cycle time of a welding process to theextent that the welding result is not worsen.

The controller 50 is a computer including one or more processors such asa central processing unit (CPU), a graphics processing unit (GPU), amulti-core CPU, etc., a read only memory (ROM), a random access memory(RAM), input/output interfaces and so on, and the respective interfacesare connected to the input unit 50 a, the output unit 50 b and thestorage device 50 c. The controller 50 performs an arc start adjustmentmethod to minimize the cycle time of the welding process by executingthe computer program 50 d stored in the storage device 50 c and causesthe computer to function as the arc start adjustment device 5.

The input unit 50 a is connected to the welding power source 2 and theimaging device 4. The welding monitor data output from the welding powersource 2 and the image data output from the imaging device 4 are inputto the input unit 50 a. The welding monitor data is time series dataindicative of, for example, welding current, welding voltage, a feedingspeed of the welding wire W, a short-circuit situation, a welding soundand the like. The image data is data representing the outer appearanceof a weld bead.

The output unit 50 b is connected to the welding robot 1 and the weldingpower source 2. The controller 50 controls a welding process and an arcstart procedure, and outputs control data to change the arc startprocedure to the welding robot 1 and the welding power source 2. Thecontrol data to change the arc start procedure may be data instructing achange of the arc start procedure or data indicating a changed arc startprocedure.

FIG. 3 is a conceptual view depicting the arc start procedure. Thecontrol device 3 starts the arc start procedure at an early stage ofmoving the welding torch 11 to the welding start position by controllingthe welding robot 1. More specifically, the control device 3 starts toslow down the wire feeding at a specific timing (t=t1) at a specificfeed speed Vs regarding a specific time point (t=t0) as a referencepoint, and supplies welding current of a specific welding current value(Is) to the welding wire W at a specific timing (t=t2), whereby arcforms to start welding. The arc start procedure includes at least astart timing t1 of slowing down the wire feeding, a wire slow down speedVs, a start timing t2 of supplying welding current and a welding currentvalue Is at the start of forming an arc, and so on.

FIG. 4 is a functional block diagram depicting the arc start adjustmentdevice 5 according to Embodiment 1. The arc start adjustment device 5includes, as functional blocks, a welding monitor data obtainment unit51 a, an image data obtainment unit 51 b, a first quality determinationunit 52 a, a second quality determination unit 52 b, a quality overalldetermination unit 54, a procedure adjustment unit 55, an arc startcontrol unit 56 and a shortest-time procedure storage unit 57.

The welding monitor data obtainment unit 51 a obtains welding monitordata output from the welding power source 2 and outputs the obtainedwelding monitor data to the first quality determination unit 52 a.

The image data obtainment unit 51 b obtains image data output from theimaging device 4 and outputs the obtained image data to the secondquality determination unit 52 b.

The first quality determination unit 52 a includes a qualitydetermination recurrent neural network (RNN) 53 a that outputs, ifwelding monitor data is input, data indicating the quality of thewelding result concerning the welding process when the welding monitordata is obtained. The quality determination RNN 53 a is a learnedrecurrent neural network, for example. The quality determination RNN 53a is stored in the storage 50 c.

The quality determination RNN 53 a includes in an output layer, forexample, a first neuron that outputs data indicative of the probabilityof the welding result being positive and a second neuron that outputsdata indicative of the probability of the welding result being negative.In this case, the above-mentioned data indicative of the quality of thewelding result is data output from the first and second neurons.

Alternatively, the quality determination RNN 53 a may include a neuronthat outputs data indicative of the quality of the welding result inbinary in the output layer. In this case, the above-mentioned dataindicative of the quality of the welding result is binary data outputfrom this neuron.

Further alternatively, the quality determination RNN 53 a may include aneuron that outputs an analog value indicative of the degree of thequality of the welding result in the output layer.

The quality determination RNN 53 a may be trained by providing arecurrent deep neural network before learning with welding monitor data(input data) and data indicating the quality of the welding resultcorresponding to the welding data (teaching data) as training data.

Note that the number of layers in an intermediate layer of the qualitydetermination RNN 53 a, the number of neurons in each layer and so onare not limited to a specific structure. Furthermore, the qualitydetermination RNN 53 a is not necessarily a recurrent neural network andmay be structured by another type of neural network.

The second quality determination unit 52 b includes a qualitydetermination convolutional neural network (CNN) 53 b that outputs, ifimage data is input, data indicative of the quality of the weldingresult concerning the welding process when the image data is obtained.The quality determination CNN 53 b is a learned convolutional neuralnetwork. The quality determination CNN 53 b is stored in the storage 50c.

The quality determination CNN 53 b includes in an output layer, forexample, a third neuron that outputs data indicative of the probabilityof the welding result being positive and a fourth neuron that outputsdata indicative of the probability of the welding result being negative.In this case, the above-mentioned data indicative of the quality of thewelding result is data output from the third and fourth neurons.

Alternatively, the quality determination CNN 53 b may include a neuronthat outputs data indicative of the quality of the welding result inbinary in the output layer. In this case, the above-mentioned dataindicative of the quality of the welding result is binary data outputfrom this neuron.

Further alternatively, the quality determination CNN 53 b may include aneuron that outputs an analog value indicative of the degree of thequality of the welding result in the output layer.

The quality determination CNN 53 b may be trained by providing aconvolutional neural network before learning with image data (inputdata) and data indicating the quality of the welding resultcorresponding to the image data (teaching data) as training data.

Note that the number of layers in an intermediate layer of the qualitydetermination CNN 53 b, the number of neurons in each layer and so onare not limited to a specific structure. Furthermore, the qualitydetermination CNN 53 b is not necessarily a convolutional neural networkand may be structured by another type of neural network.

The quality overall determination unit 54 determines the quality of thewelding result based on the data output from the first qualitydetermination unit 52 a and the second quality determination unit 52 b,and outputs the determination result to the procedure adjustment unit55.

For example, the quality overall determination unit 54 performs overalldetermination based on the data output from the first and second neuronsof the quality determination RNN 53 a and the data output from the thirdand fourth neurons of the quality determination CNN 53 b. Morespecifically, the quality determination of the welding result may bemade by comparing the sum of the data values output from the firstneuron and the third neuron and the sum of the data values output fromthe second neuron and the fourth neuron. Furthermore, the values outputfrom the respective neurons may be weighted and summed and then may becompared with one another.

In addition, in the case where the quality determination RNN 53 a andthe quality determination CNN 53 b are configured to output binary data,if both of the first quality determination unit 52 a and the secondquality determination unit 52 b output data indicative of a positiveresult, it is determined as “positive,” while if either the firstquality determination unit 52 a or the second quality determination unit52 b outputs data indicative of a negative result, it is determined as“negative.” Note that the overall determination method is one example,and it may be configured to determine as “positive” if either the firstquality determination unit 52 a or the second quality determination unit52 b outputs data indicative of a positive result.

The procedure adjustment unit 55 adjusts the arc start procedure suchthat the cycle time of the welding process is shortened if the resultdetermined by the quality overall determination unit 54 is positivewhile the cycle time of the welding process is extended if thedetermination result is negative, and outputs the adjustment result tothe arc start control unit 56. The adjustment result is, for example,data indicative of increase or decrease of various parameters of the arcstart procedure, such as a start timing t1 of slowing down the wirefeeding, a wire slow down speed Vs, a start timing t2 of supplyingwelding current and a welding current value Is at the start of formingan arc and so on. The procedure adjustment unit 55 outputs dataindicative of increasing or decreasing at least one of the variousparameters of the arc start procedure to the arc start control unit 56.

The procedure adjustment unit 55 may change values of multipleparameters or a value of one parameter, by a single adjustmentprocessing. In the case where an arc start procedure is performed byrepeatedly executing the processing from steps S11 to S19 describedlater until the cycle time is minimized, a different parameter may beadjusted in each adjustment processing repeatedly executed. For example,at the first adjustment, the start timing of slowing down the wirefeeding may be adjusted, and at the second adjustment, the wire slowdown speed may be adjusted.

Such a configuration may be taken that the variables may be reduced bybrining increased and decreased amounts of the parameters intocorrelation with each other. For example, in the case where the wireslow down speed Vs is increased, the start timing of supplying weldingcurrent t2 may be made fast, that is, be slightly changed. Moreover,such a configuration may be taken that the change amount is restrictedto a range within a predetermined rate of a standard parameter value.

In addition, in the case where the welding result changes from apositive state to a negative state as a result of the procedureadjustment unit 55 determining that the cycle time of the weldingprocess is shortened, the procedure adjustment unit 55 stores the arcstart procedure before the cycle time is shortened in the shortest-timeprocedure storage device 57.

The arc start control unit 56 controls the arc start by outputting tothe welding power source 2 control data to change the arc startprocedure based on the adjustment result by the procedure adjustmentunit 55. Note that if the shortest-time procedure storage device 57 hasstored the arc start procedure that makes the cycle time of the weldingprocess shortest, the arc start control unit 56 controls an arc startbased on the arc start procedure stored in the shortest-time procedurestorage device 57.

The following describes a processing procedure performed by thecontroller 50 relating to the adjustment of the arc start procedure.

FIG. 5 is a flowchart depicting an arc start adjustment method accordingto Embodiment 1. The controller 50 repeatedly executes, for example, thefollowing processing for every welding process. The controller 50determines whether or not the arc start procedure that makes the cycletime shortest is stored in the storage device 50 c (step S11). Ifdetermining that the arc start procedure that makes the cycle timeshortest is stored (step S11: YES), the controller 50 performs an arcstart based on the shortest arc start procedure stored in the storagedevice 50 c (step S12). For example, the controller 50 controls the arcstart by outputting the control data indicative of the shortest arcstart procedure to the welding power source 2. It is understood that thecontroller 50 may control the arc start by outputting control dataindicative of the change amount to make the cycle time shortest to thewelding power source 2.

If determining that the arc start procedure that makes the cycle timeshortest is not stored in the storage device 50 c (step S11: NO), thecontroller 50 obtains welding monitor data (step S13) and obtains imagedata (step S14). The controller 50 then determines the quality of thewelding result based on the obtained welding monitor data and image data(step S15). For example, the controller 50 determines the quality of thewelding result by using the learned quality determination RNN 53 a andthe learned quality determination CNN 53 b.

Next, if determining that the welding result is positive (step S15:YES), the controller 50 shortens the cycle time of the welding process(step S16). If determining that the welding result is negative (stepS15: NO), the controller 50 determines whether or not the welding resultchanges from a positive state to a negative state at the previous timeas a result of the welding process being shortened (step S17).Obviously, the change to the negative state may be determined by two ormore welding results without being restricted to be determined by asingle welding result. For example, if the welding result is negative ina certain number of times or more out of ten times, a change to thenegative state may be determined. If determining that the welding resultdoes not change from a positive state to a negative state (step S17:NO), the controller 50 extends the cycle time of the welding process(step S18). The controller 50 having completed the processing at stepS16 or S18 controls the arc start based on the adjusted arc startprocedure (step S19). More specifically, the controller 50 controls arcstart by outputting to the welding power source 2 the control dataindicative of the arc start procedure after the adjustment processing.The controller 50 may control the arc start by outputting to the weldingpower source 2 control data indicative of the change amount for the arcstart procedure.

If determining that the welding result changes from a positive state toa negative state as a result of the cycle time of the welding processbeing shortened (step S17: YES), the controller 50 restores the cycletime of the welding process to that of the arc start procedure beforeshortening (step S20), stores the arc start procedure before the cycletime is shortened as an arc start procedure with the shortest cycle timein the storage device 50 c (step S21) and returns the processing to stepS12.

The arc start adjustment device 5 thus configured, the welding system,the arc start adjustment method and the computer program 50 d mayeffectively shorten the cycle time of the welding process withoutworsening the welding result.

Moreover, since the minimized arc start procedure is configured to bestored in the storage device 50 c, the arc start adjustment device 5 maycontrol the welding by promptly minimizing the cycle time of the weldingprocess henceforth.

Note that Embodiment 1 described an example that the arc startadjustment device 5 is provided with the learned quality determinationRNN 53 a and the learned quality determination CNN 53 b. However, such aconfiguration may be employed that various parameters defining theneural networks of the first quality determination unit 52 a and thesecond quality determination unit 52 b may be downloaded from anexternal server for an update. The parameters are information including,for example, the number of layers in an intermediate layer, the numberof neurons in each layer, the weighting factor of each neuron, the kindof an active function, and the like. Furthermore, the arc startadjustment device 5 may be configured to store a flag indicating whetheror not the downloaded various parameters are permitted to be reflectedon the first quality determination unit 52 a and the second qualitydetermination unit 52 b, and to update the neural networks of thequality determination RNN 53 a and the quality determination CNN 53 b byusing the downloaded parameters if the flag indicates permission.

Moreover, if multiple welding systems each provided with an arc startadjustment device 5 are installed in a factory, the arc start adjustmentdevices 5 of the welding systems may exchange the above-mentionedparameters with each other as necessary.

Additionally, the arc start adjustment device 5 may be structured as acloud server. The welding power source 2 or the control device 3 mayrequest the server to adjust the arc start procedure, receive anadjustment amount of the arc start procedure transmitted from the serverin response to the request and adjust the arc start procedure.

In addition, the arc start adjustment device 5 may be provided in thewelding power source 2. Furthermore, the arc start adjustment device 5may be implemented as a dedicated device for the arc start procedureadjustment. The operator may automatically adjust the arc startprocedure by connecting this dedicated device to the welding system.

Moreover, such an example is described that the first qualitydetermination unit 52 a and the second quality determination unit 52 bare respectively provided with the quality determination RNN 53 a andthe quality determination CNN 53 b, but both or either one of thedetermination units may be configured to determine the quality of thewelding result without using a neural network. For example, the firstquality determination unit 52 a may determine the quality of the weldingresult by such simple determination processing of comparing a weldingcurrent value and a predetermined threshold. Furthermore, the secondquality determination unit 52 b may determine the quality of the weldingresult by such simple determination processing of extracting apredetermined feature value from the image data and comparing thepresence or absence of the feature value, the numeral of the featurevalue, etc. and the threshold. Moreover, it is not necessary to provideboth of the first quality determination unit 52 a and the second qualitydetermination unit 52 b, and either one of them may be provided. In thiscase, the quality overall determination unit 54 is not required.

Embodiment 2

FIG. 6 is a functional block diagram depicting an arc start adjustmentdevice 205 according to Embodiment 2. The arc start adjustment device205 according to Embodiment 2 includes a welding monitor data obtainmentunit 51 a, an image data obtainment unit 51 b, a first qualitydetermination unit 52 a, a second quality determination unit 52 b, aquality overall determination unit 254, a procedure adjustment unit 255and an arc start control unit 56 similarly to Embodiment 1, and furtherincludes a learning processing unit 259.

The arc start adjustment device 205, the welding system, the arc startadjustment method and the computer program 50 d according to Embodiment2 are different from those of Embodiment 1 in that the procedureadjustment unit 55 and the shortest-time procedure storage unit 57according to Embodiment 1 are structured as a deep neural network, andthus the following mainly describes the above-mentioned differences.Since the other configurations and effects are the same as those inEmbodiment 1, the same reference numerals are given to the correspondingparts while the description thereof is omitted.

The welding monitor data obtainment unit 51 a obtains welding monitordata output from the welding power source 2 and outputs the obtainedwelding monitor data to the first quality determination unit 52 a andthe procedure adjustment unit 255.

The image data obtainment unit 51 b obtains image data output by theimaging device 4 and outputs the obtained image data to the secondquality determination unit 52 b and the procedure adjustment unit 255.

The quality overall determination unit 254 according to Embodiment 2outputs data indicative of the probability of the welding result beingpositive and data indicative of the probability of the welding resultbeing negative to the learning processing unit 259. For example, theprobability of the welding result being positive has only to becalculated by using the data value output from the first neuron of thequality determination RNN 53 a and the data value output from the thirdneuron of the quality determination CNN 53 b. Similarly, the probabilityof the welding result being negative has only to be calculated by usingthe data value output from the second neuron of the qualitydetermination RNN 53 a and the data value output from the fourth neuronof the quality determination CNN 53 b.

The procedure adjustment unit 255 includes a procedure adjustmentneutral network (NN) 258 that outputs data indicative of a change amountof the arc start procedure that is capable of shortening the cycle timeif welding monitor data and image data are input. The procedureadjustment NN 258 is a learned deep neural network. The procedureadjustment NN 258 is stored in the storage 50 c.

The procedure adjustment NN 258 includes multiple neurons in an outputlayer each outputting data indicative of the probability of eachadjustment amount being suitable, for each of the multiple adjustmentamounts relative to various adjustment parameters such as a start timingt1 of slowing down the wire feeding, a wire slow down speed Vs, awelding current value Is at the start of forming an arc, a start timingt2 of supplying welding current and so on.

Alternatively, the procedure adjustment NN 258 may be configured toinclude a neuron that outputs data indicative of an adjustment amount inthe output layer. Further alternatively, the procedure adjustment NN 258may be configured to include a neuron that outputs an adjustment amountin binary data in the output layer. In Embodiment 2, the procedureadjustment NN 258 is assumed to output not binary data but dataindicative of the probability of the change amount for each of theparameters being suitable henceforth.

Note that the procedure adjustment unit 255 may change multipleparameter values or may change one parameter value, by single adjustmentprocessing. Alternatively, such a configuration may be employed that adifferent parameter value is adjusted by each adjustment processingrepeatedly performed.

FIG. 7 is a conceptual view depicting the network structure of theprocedure adjustment unit 255. The procedure adjustment NN 258 of theprocedure adjustment unit 255 includes a welding state recognitionnetwork part 258 a, an external appearance state recognition networkpart 258 b and a procedure adjustment network part 258 c.

The welding state recognition network part 258 a is a neural networkthat receives an input of welding monitor data, recognizes a weldingstate during a welding process, and outputs data corresponding to thisstate. If the welding monitor data is welding current, the welding staterecognition network part 258 a can recognize a changed state of thewelding current. The welding state recognition network part 258 a mayhave a similar neural network structure to the first qualitydetermination unit 52 a except for the output layer, for example. Theoutput layer includes multiple neurons, preferably three or moreneurons. Furthermore, the weighting factors of the neurons constitutingthe first quality determination unit 52 a may be set as initial valuesof the weighting factors before learning. Thus, the procedure adjustmentunit 255 can be trained more efficiently.

The external appearance state recognition network part 258 b is a neuralnetwork that receives an input of image data, recognizes the state ofthe welding region after welding, and outputs data corresponding to thisstate. The external appearance state recognition network part 258 b mayhave a similar neural network structure to the second qualitydetermination unit 52 b except for the output layer, for example. Theoutput layer includes multiple neurons, preferably three or moreneurons. The weighting factors of the neurons constituting the secondquality determination unit 52 b may be set as initial values of theweighting factors before learning. Thus, the procedure adjustment NN 258can be trained more efficiently.

The procedure adjustment network part 258 c is a learned neural networkthat receives inputs of data output from the welding state recognitionnetwork part 258 a and the external appearance state recognition networkpart 258 b, and outputs data indicative of the change amount of the arkstart procedure that may be shortened. This neural network is preferablystructured by a deep neural network including multiple intermediatelayers.

The structure of the neural network of the procedure adjustment unit 255is one example, and the neural network may be structured by a singleneural network or by multiple neural networks in combination.

The learning processing unit 259 is a processing unit to train theprocedure adjustment NN 258 regarding the welding monitor data and theimage data input to the procedure adjustment unit 255 as input data andregarding data indicative of the quality of the welding result when thearc start procedure is changed based on the data, as training data.

More specifically, the learning processing unit 259 trains the procedureadjustment NN 258 such that the cycle time is shortened if the weldingresult is positive, the cycle time is extended if the welding result isnegative, and the cycle time is maintained if the welding result is in amoderate state between the positive state and the negative state,according to the determination result by the quality overalldetermination unit 254.

The state of the welding result being positive is, for example, a statewhere the probability of the welding result being positive is equal toor more than 50%, and the probability of the welding result beingnegative is less than 50%. The state of the welding result beingnegative is, for example, a state where the probability of the weldingresult being positive is less than 50%, and the probability of thewelding result being negative is equal to or more than 50%. Thethreshold of 50% is one example and it may take a value larger than 50%.

The state of the welding result being moderate is, for example, a statewhere the probability of the welding result being positive and theprobability of the welding result being negative are both equal to ormore than 50%, or a state where both of them are less than 50%. Inaddition, if the above-described threshold is larger than 50%, forexample, 60%, a state where the probability of the welding result beingpositive and the probability of the welding result being negative areboth between 40% and 60% is also the moderate state. Note that such amoderate state is one example. The moderate state is a state where thewelding result is likely to be worsen if the cycle time is shortened anyfurther.

As discussed above, the procedure adjustment NN 258 is trained tothereby minimize the cycle time of the welding process without worseningthe welding result.

Note that at the initial stage of the learning by the procedureadjustment NN 258, the arc start procedure may arbitrarily be changedwithout maintaining the cycle time in the case of the moderate state.

Here, the procedure adjustment NN 258 may be trained at an appropriatetiming when a welding system is installed, when an external environmentchanges, when welding conditions are changed, or when replacement isperformed, for example.

In addition, Embodiment 2 described an example that the procedureadjustment NN 258 is trained as an example, but a learned procedureadjustment NN 258 may be provided so as not to be further trained.

According to the arc start adjustment device 205, the welding system,the arc start adjustment method and the computer program 50 d thusconfigured in Embodiment 2, the procedure adjustment unit 255constituted by a deep neural network is configured to decide the changeamount of the arc start procedure, so that the arc start in the weldingprocess can be controlled more appropriately and can be shortenedwithout worsening the welding result.

Furthermore, the procedure adjustment unit 255 can output the changeamount of the arc start procedure that shortens the cycle time of thewelding process. The procedure adjustment unit 255 can output a largechange amount if the welding result is extremely stable, and output asmall change amount if the welding result is positive but unstable, forexample. Accordingly, the cycle time of the welding process can beminimized more swiftly.

Moreover, the procedure adjustment unit 255 can be trained by using thequality determination of the welding result, and thus it can be adjustedso as to be suitable for the environment where the welding system isinstalled. Accordingly, the cycle time of the welding process can beminimized depending on the welding condition and the externalenvironment.

Additionally, the learning processing unit 259 can train the procedureadjustment unit 255 in the direction in which the cycle time of thewelding process is shortened to thereby output data that is capable ofminimizing the cycle time of the welding process, and then train theprocedure adjustment neural network to stably obtain a positive weldingresult. Accordingly, the welding result can be kept in a positive statewhile the cycle time of the welding process can be minimized.

Note that Embodiment 2 described an example that the arc startadjustment device 205 includes the learned procedure adjustment NN 258,but various parameters defining the neural network of the procedureadjustment unit 225 may be downloaded from an external server for anupdate. The parameters are information including, for example, thenumber of layers in an intermediate layer, the number of neurons in eachlayer, the weighting factor of each neuron, the kind of an activefunction, and so on. Furthermore, the arc start adjustment device 205may be configured to store a flag indicating whether or not thedownloaded various parameters are permitted to be reflected on theprocedure adjustment unit 255, and to update the neural network of theprocedure adjustment NN 258 using the downloaded parameters if the flagindicates permission.

Moreover, if multiple welding systems each provided with arc startadjustment device 205 are installed in a factory, the arc startadjustment devices 205 of the welding systems may exchange theabove-mentioned parameters with each other as necessary.

In addition, the arc start adjustment device 205 may be configured toupload the various parameters defining the learned procedure adjustmentNN 258 to the external server. Another arc start adjustment device 205may update the procedure adjustment NN 258 by using the parametersuploaded to the server.

Note that Embodiment 2 described an example that the procedureadjustment NN 258 as well as the first quality determination unit 52 aand the second quality determination unit 52 b each include a neuralnetwork, but both of the quality determination RNN 53 and the qualitydetermination CNN 53 b or either one of them may be configured todetermine the quality of the welding result without using a neuralnetwork.

Embodiment 3

FIG. 8 is a functional block diagram depicting an arc start adjustmentdevice 305 according to Embodiment 3. The arc start adjustment device305, the welding system, the arc start adjustment method and thecomputer program 50 d according Embodiment 3 are different fromEmbodiment 2 in data to be input to a procedure adjustment unit 355, andthus the difference will mainly be described below. Since the otherconfigurations and effects are the same as those in the embodimentsdescribed herein, the same reference numerals are given to thecorresponding parts, and the description thereof is omitted.

The arc start adjustment device 305 according to Embodiment 3 is furtherprovided with a welding condition data obtainment unit 51 c. The weldingcondition data obtainment unit 51 c obtains welding condition data. Thewelding condition data includes information such as a material of thebase metal A, a shape of the groove, a setting value for weldingcurrent, a setting value for welding voltage, a setting value for awelding speed, a setting value for frequency when welding current isperiodically varied and so on.

The procedure adjustment unit 355 includes a learned procedureadjustment NN 358 that outputs data indicative of the change amount ofthe arc start procedure that is capable of shortening the cycle time ofthe welding process without worsening the welding result based on theinput welding monitor data and image data as well as the weldingcondition data. The learned procedure adjustment NN 358 according toEmbodiment 3 may be further trained by using a quality overalldetermination unit 354 and a learning processing unit 359 similarly toEmbodiment 2.

According to the arc start adjustment device 305, the welding system,the arc start adjustment method and the computer program 50 d inEmbodiment 3, the arc start adjustment procedure is adjusted taking thewelding conditions into account, so that the arc start procedure may beadjusted more effectively.

Embodiment 4

FIG. 9 is a functional block diagram depicting an arc start adjustmentdevice 405 according to Embodiment 4. The arc start adjustment device405, the welding system, the arc start adjustment method and thecomputer program 50 d according to Embodiment 4 are different fromEmbodiment 1 in that the procedure adjustment unit 455 and theshortest-time procedure storage unit 57 according to Embodiment 4 areconfigured to train the arc start procedure by deep reinforcementlearning, and thus the difference will mainly be discussed below. Sincethe other configurations and effects are the same as those in theembodiments described herein, the same reference numerals are given tothe corresponding parts while the description thereof is omitted.

The arc start adjustment device 405 according to Embodiment 4 includes awelding monitor data obtainment unit 51 a, an image data obtainment unit51 b, a state data obtainment unit 51 d, a first quality determinationunit 52 a, a second quality determination unit 52 b, a quality overalldetermination unit 54, the procedure adjustment unit 455 and an arcstart control unit 56.

The state data obtainment unit 51 d obtains state data indicative of thestate (s) of a welding system. The state data includes, for example,image data, e. g., moving image data obtained by imaging the weldingtorch 11, the welding wire W and the base metal A at multiple timepoints in the arc start processing. The welding torch 11, the weldingwire W and the base metal A may be imaged by the imaging device 4 or maybe imaged by another moving image shooting device. Such a configurationis preferably employed that the moving image data includes image datathat allows for image recognition of the state (s) of the weldingsystem, that is, the positional relation among the welding torch 11, thewelding wire W and the base metal A.

The procedure adjustment unit 455 is for training the arc startprocedure that minimizes the cycle time of the welding process by deepreinforcement learning and is provided with an evaluation unit 455 a, anaction selection unit 455 b, a reward calculation unit 455 c and areinforcement learning unit 455 d.

The evaluation unit 455 a is a computation function unit to calculate anevaluation value Q relative to an action (a) in a state indicated by thestate data, based on the state data obtained by the state dataobtainment unit 51 d and the action data indicative of the action (a)concerning the arc start procedure. The state is, for example, apositional relation among the welding torch 11, the welding wire W andthe base metal A or an image representing the positional relation. Theaction (a) concerning the arc start procedure is decided by a start ofslowing down the wire feeding, a wire feeding speed, a start ofsupplying welding current, a welding current value, etc. The evaluationvalue Q increases as the cycle time of the welding process can suitablybe shortened and the welding result is better when a specific action (a)is taken in the case where the welding torch 11, the welding wire W andthe base metal A are in a specific positional relation.

The evaluation unit 455 a includes an evaluation neural network (NN) 455e that outputs, in the case where state data indicative of a state (s)of the welding system obtained by the state data obtainment unit 51 dand action data indicative of an action (a) concerning the arc startprocedure are input, an evaluation value Q (s, a) relative to the actionin the state (s). the evaluation NN 455 e is stored in the storage 50 c.

Note that the evaluation NN 455 e may be provided with a convolutionalneural network at the preceding stage to recognize state datarepresenting the state of the welding system by images.

The action selection unit 455 b selects an action (a) taken when theevaluation value Q calculated by the evaluation unit 455 a in a certainstate (s) is maximum. The procedure adjustment unit 455 performsadjustment of the arc start procedure based on the action (a) selectedby the action selection unit 455 b, and the arc start control unit 56controls the arc start by the adjusted arc start procedure.

The reward calculation unit 455 c calculates a reward for the arc startprocedure based on the determination result output from the qualityoverall determination unit 54 and the time from when the welding torch11 reaches a welding region to when the arc forms. The reward iscalculated so as to increase as the welding result is better and thetime until arc forms is shorter. The arithmetic expression to calculatea reward is not limited to a specific one.

The reinforcement learning unit 455 d trains the evaluation NN 455 ebased on the state data and action data that are input to the evaluationNN 455 e, the evaluation value Q output when respective data are inputand the reward calculated by the reward calculation unit 455 c. Morespecifically, the weighting factor of a neural network may be trained bythe evaluation value Q represented by the following formula (1).

Q(s,a)←Q(s,a)+α(r+γ max Q(s_next,a_next)−Q(s,a))  (1)

where“s” represents a state;“a” represents an action selected in the state (s);“α” represents a learning coefficient;“r” represents a reward obtained as a result of the action;“γ” represents a discount rate;“max Q (s_next, a_next)” represents a maximum value of the evaluationvalue Q for the action that will be taken in the next state.

The learning coefficient α is a positive value being equal to or lessthan 1 and a value on the order of 0.1, for example. The discount rate γis a positive value equal to or less than 1 and a value on the order of0.9, for example.

By the machine learning using the above-mentioned formula (1), theevaluation NN 455 e can be trained such that a higher evaluation value Qis given to the action (a) from which a higher reward may be obtained.Note that upon reinforcement learning, a ε⁻ greedy method or the likemay be employed in which random actions are taken at a certainprobability, and Q values for the various actions may be trained.

According to the arc start adjustment device 405 thus configured, theaction selection unit 455 b can select a more appropriate action (a),i.e., an action of starting to slow down the wire feeding and a wireslow down speed, an action of starting to supply welding current and awelding current value according to the state (s) of the welding system,i.e., the positional relation among the welding torch 11, the weldingwire W and the base metal A, and can minimize the cycle time of thewelding process.

According to the arc start adjustment device 405, the welding system,the arc start adjustment method and the computer program 50 d inEmbodiment 4, deep reinforcement learning of the arc start procedurethat shortens the cycle time of the welding process is made possible.

Note that the deep reinforcement learning is described in theabove-mentioned Embodiment 4, but such a configuration may be employedthat the array of evaluation values Q corresponding to actions andstates is provided in place of the neural network to thereby adjust thearc start procedure.

It is to be noted that, as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise.

It is to be noted that the disclosed embodiment is illustrative and notrestrictive in all aspects. The scope of the present disclosure isdefined by the appended claims rather than by the description precedingthem, and all changes that fall within metes and bounds of the claims,or equivalence of such metes and bounds thereof are therefore intendedto be embraced by the claims

What is claimed is:
 1. An arc start adjustment device adjusting an arcstart procedure in a welding process comprising: one or more processors;a storage storing instructions for causing the one or more processorsto: obtain welding data indicating a welding state during or after awelding process, and adjust the arc start procedure such that a cycletime of the welding process is shortened based on the welding data. 2.The arc start adjustment device according to claim 1 further comprisinginstructions for causing the one or more processors to determine whethera welding result is positive or negative based on the welding data,wherein the instructions to adjust the arc start procedure compriseinstructions to decide a change in the arc start procedure such that thecycle time is shortened if the welding result is positive, and the cycletime is extended if the welding result is negative.
 3. The arc startadjustment device according to claim 2, wherein the instructions toadjust the arc start procedure comprise instructions to fix the arcstart procedure at a previous arc start procedure if the welding resultchanges from a positive state to a negative state as a result ofshortening the cycle time of the welding process, and store a fixed arcstart procedure in a storage device.
 4. The arc start adjustment deviceaccording to claim 2, wherein the storage stores a quality determinationneural network that is a trained neural network to output dataindicating quality of a welding result when the welding data is input;and the instructions to determine whether a welding result is positiveor negative comprise instructions to determine that by using the qualitydetermination neural network.
 5. The arc start adjustment deviceaccording to claim 1, wherein the storage stores a procedure adjustmentneural network that is a trained neural network to output dataindicating a change in the arc start procedure that is capable ofshortening the cycle time of the welding process when the welding datais input; and the instructions to adjust the arc start procedurecomprise instructions to adjust the arc start procedure by using theprocedure adjustment neural network.
 6. The arc start adjustment deviceaccording to claim 5, wherein the procedure adjustment neural networkoutputs data indicating a change amount of the arc start procedure. 7.The arc start adjustment device according to claim 5, further comprisinginstructions for causing the one or more processors to: determinewhether a welding result is positive or negative based on the weldingdata; and train the procedure adjustment neural network based on theresult obtained after the arc start procedure is adjusted.
 8. The arcstart adjustment device according to claim 7, wherein the instructionsto train the procedure adjustment neural network comprise instructionsto train the procedure adjustment neural network such that the cycletime is shortened if the welding result is positive and the cycle timeis extended if the welding result is negative.
 9. The arc startadjustment device according to claim 8, wherein the instructions totrain the procedure adjustment neural network comprise instructions totrain the procedure adjustment neural network such that the cycle timeis maintained if the welding result is in a moderate state betweenpositive and negative states.
 10. The arc start adjustment deviceaccording to claim 7, wherein the storage stores a quality determinationneural network that is a trained neural network to output dataindicating quality of a welding result when the welding data is input;and the instructions to determine whether a welding result is positiveor negative comprise instructions to determine that by using the qualitydetermination neural network.
 11. The arc start adjustment deviceaccording to claim 10, wherein the procedure adjustment neural networkincludes a network structure substantially the same as all or a part ofthe quality determination neural network.
 12. The arc start adjustmentdevice according to claim 1 further comprising instructions for causingthe one or more processors to: determine whether a welding result ispositive or negative based on the welding data; and obtain state dataincluding image data obtained by imaging a welding torch, a welding wireand a base metal at multiple time points in arc start processing,wherein the instructions to adjust the arc start procedure compriseinstructions to: calculate, an evaluation value for an action concerningthe arc start procedure in a state indicated by the state data based onthe state data and action data indicating the action; select an actionfor which the evaluation value is maximum; calculate a reward for thearc start procedure based on a result obtained after the arc startprocedure is adjusted and a time from when the welding torch reaches awelding region to when an arc forms; and train based on the state data,the action data and the reward.
 13. The arc start adjustment deviceaccording to claim 12, wherein the storage stores an evaluation neuralnetwork to output an evaluation value for the action in the stateindicated by the state data when the state data and the action data arcstart procedure are input; and the instructions to calculate the rewardcomprise instructions to calculate the reward by using the evaluationneural network.
 14. The arc start adjustment device according to claim1, wherein the welding data includes data indicating at least one ofwelding current and welding voltage detected during the welding process,a feeding speed of a welding wire, a short-circuit situation, a weldingsound collected during the welding process and an image of a weldingregion imaged after the welding process.
 15. The arc start adjustmentdevice according to claim 1, wherein the arc start procedure includes atleast one of a start timing of slowing down the wire feeding, a wireslow down speed, a start timing of supplying welding current and awelding current value in arc start.
 16. A welding system comprising: thearc start adjustment device according to claim 1; a welding robot with awelding torch; and a welding power source that supplies welding currentto the welding torch.
 17. An arc start adjustment method of adjusting anarc start procedure in a welding process, comprising: obtaining weldingdata indicating a welding state during or after the welding process; andadjusting the arc start procedure such that a cycle time of the weldingprocess is shortened based on obtained welding data.